Dual Branch Encoding Feature Aggregation for Cloud and Cloud Shadow Detection of Remote Sensing Image
Cloud detection is a critical preprocessing step for optical remote sensing imagery. However, traditional CNN-based methods have limitations in global feature modeling, while Transformer models, despite their strong global modeling capability, struggle to capture fine-grained local details effective...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/11/6343 |
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| Summary: | Cloud detection is a critical preprocessing step for optical remote sensing imagery. However, traditional CNN-based methods have limitations in global feature modeling, while Transformer models, despite their strong global modeling capability, struggle to capture fine-grained local details effectively. To tackle these challenges, this study introduces a dual-path neural network framework that synergistically combines convolutional neural networks (CNNs) and architectures. By capitalizing on their complementary strengths, this work proposed a dual-branch feature extraction architecture that utilizes two different feature aggregation modes to effectively aggregate features of CNN and Transformer at different levels. Specifically, two novel modules are introduced: the Dual-branch Lightweight Aggregation Module (DLAM), which fuses CNN and Transformer features in the early encoding stage and emphasizes key information through a feature weight allocation mechanism; and the Dual-branch Attention Aggregation Module (DAAM), which further integrates local and global features in the late encoding stage, improving the model’s differentiation performance between cloud and cloud shadow areas. The collaboration between DLAM and DAAM enables the model to efficiently learn multi-scale and spatially hierarchical information, thereby improving detection performance. The experimental findings validate the superior performance of our model over state-of-the-art methods on diverse remote sensing datasets, attaining enhanced accuracy in cloud detection. |
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| ISSN: | 2076-3417 |